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Chronic social defeat stress induces meningeal neutrophilia via type I interferon signaling: single cell RNA sequencing data

Meningeal single cell RNA sequencing data Meningeal samples were collected from both dorsal and ventral skull, avoiding inclusion of choroid plexus. Samples were digested in 2.5 mg/mL Collagenase D (Cat. #11088858001; Roche) and 12.5 μL of 0.5 mg/mL DNAseI (Cat. #L5002139; Worthington), put on a shaker at 370C for 30 m, diluted with cold HBSS + 0.1% BSA, and mashed through a 70 μm cell strainer prior to sorting. Data represent live, nucleated, singlet cells (DAPI-DRAQ5+) sorted on a BD FACS Aria Fusion into HBSS + 10% FBS prior to droplet encapsulation using 10x Genomics’ Drop-seq platform (Chromium v2). 10X chip lane is indicate by 'group' column Group 1 = 4 pooled homecage control (unstressed) mice Group 2 = 4 pooled homecage control (unstressed) mice Group 3 = 4 pooled mice exposed to chronic social defeat for 14 days; tissue was collected 2 hours following final defeat See the following repositories for data processing: <a href="https://github.com/maryellenlynall/2019_bcell_stress/blob/master/bcellstress20.Rmd">https://github.com/maryellenlynall/2019_bcell_stress/</a> (processing from raw files starts at bcellstress020.Rmd) <a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a> We also provide a processed dataset (processed.RData) with assays 'counts' and 'logcounts' which is the processed single cell object saved at line "# Save object for upload to Zenodo" in script <a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a>neutrophilstress01.Rmd  Cluster annotations are in sce$Annotation Neutrophil subcluster annotations are in sce$Subcluster Sample condition is in sce$cond, where "HC" indicates homecage control and "SD" indicates chronic social defeat 10X chip lane is in sce$group

Data for paper: Analysis and control of untemplated DNA polymerase activity for guided synthesis of kilobase-scale DNA sequences

This deposit contains data for the paper entitled: "Analysis and control of untemplated DNA polymerase activity for guided synthesis of kilobase-scale DNA sequences" Contents include: <ul> <li>00_sequencing-metadata.xlsx - Metadata for each sequencing sample.</li> <li>01_raw-fastqs.zip - Raw basecalled FASTQ data.</li> <li>02_clean-fastas.zip - Cleaned FASTA files (removal of adapters and barcode sequences).</li> <li>03_read-statistics.zip - Read statistics for all samples.</li> <li>04_sequence-analysis.zip - Sequence analysis output for all samples.</li> <li>05_qpcr-data.xlsx - qPCR data for all the dNTP mixes studied.</li> <li>06_analysis-scripts.zip - Analysis scripts.</li> </ul>

opencc-by-4.0Mar 2023View details →

Figure 1 in First record of Parochlus kiefferi (Garrett, 1925) in a sediment sequence from a Slovak mountain lake with notes on paleolimnological interpretation

Figure 1. View of lake Vrbické pleso. Photo: Ladislav Hamerlík.

opencc-by-4.0Jun 2024View details →

Distribution of Chlamydia trachomatis ompA-genotypes over three decades (1990-2021) in Portugal - ompA sequence datasets

This repository includes sequence data from the study: "Distribution of Chlamydia trachomatis ompA-genotypes over three decades (1990-2021) in Portugal”, conducted by the National Reference Laboratory (NRL) for Sexually Transmitted Infections (STI), National Institute of Health Doutor Ricardo Jorge (INSA), Portugal. The NRL performs molecular characterization (namely ompA-genotyping) of all C. trachomatis positive samples that receives. C. trachomatis ompA-genotyping technique was adapted from Lan, et al. [1]. Briefly, PCR and nested PCR were performed using primers NLO and NRO, and primers PCTM3 and SERO2A, respectively, as previously described [2]. Partial nucleotide sequencing of the ~1010 bp PCR product was performed with BigDye terminator v1.1 and capillary sequencing (3130XL Genetic Analyzer; Applied Biosystems),  using either two primers, as described elsewhere [2,3,4], or one primer (since ~2018), as described elsewhere [5]. Until 2018, LaserGene (DNASTAR) and MEGA (http://www.megasoftware.net) software were applied for sequence curation, alignment and phylogenetic reconstructions involving multiple reference sequences, as previously described [3]. Since ~2018, ompA genotypes have been determined by BLASTn-based comparison (using the ABRIcate tool [6]) directly from raw Sanger (AB1 format) sequences, with a custom database enrolling reference and variant sequences of all main ompA genotypes (<a href="https://github.com/insapathogenomics/ReporType/blob/main/databases/c_trachomatis.fasta">https://github.com/insapathogenomics/ReporType/blob/main/databases/c_trachomatis.fasta</a>) [3, 5, 7, 8], as currently implemented in ReporType (<a href="https://github.com/insapathogenomics/ReporType">https://github.com/insapathogenomics/ReporType</a>) [8]. When needed, MEGA software is then applied for fine genotype confirmation, namely for L2/L2b discrimination and confirmation of the hybrid ompA-profile of the recombinant L2b/D-Da [5]. This repository includes the following sequence datasets: <ul> <li>Dataset 1 - ompA sequences (curated FASTA) of C. trachomatis positive samples collected between 1991 and ~2018, as described above.</li> <li>Dataset 2 - ompA sequences (raw Sanger sequences, converted from “ab1” format to FASTA) of C. trachomatis positive samples collected since ~2018 and 2021, as described above.</li> </ul> Note: The associated metadata is described in the Supplementary table 2 of the manuscript. These sequence datasets do not cover genotyped samples for which the ompA sequences were lost over the three decades of the laboratory's historical collection.   References 1. Lan J, Ossewaarde JM, Walboomers JM, Meijer CJ, van den Brule AJ. Improved PCR sensitivity for direct genotyping of Chlamydia trachomatis serovars by using a nested PCR. J Clin Microbiol. 1994;32(2):528-530. doi:10.1128/jcm.32.2.528-530.1994; 2. Gomes JP, Bruno WJ, Borrego MJ, Dean D. Recombination in the genome of Chlamydia trachomatis involving the polymorphic membrane protein C gene relative to ompA and evidence for horizontal gene transfer. J Bacteriol 2004;186:4295–4306; 3. Nunes A, Borrego MJ, Nunes B, Florindo C, Gomes JP. Evolutionary dynamics of ompA, the gene encoding the Chlamydia trachomatis key antigen. J Bacteriol. 2009 Dec;191(23):7182-92. doi: 10.1128/JB.00895-09. Epub 2009 Sep 25. PMID: 19783629; PMCID: PMC2786549; 4. Nunes A, Nogueira PJ, Borrego MJ, Gomes JP. Adaptive evolution of the Chlamydia trachomatis dominant antigen reveals distinct evolutionary scenarios for B- and T-cell epitopes: worldwide survey. PLoS One. 2010 Oct 5;5(10):e13171. doi: 10.1371/journal.pone.0013171. PMID: 20957150; PMCID: PMC2950151. 5. Borges V, Cordeiro D, Salas AI, et al. Chlamydia trachomatis: when the virulence-associated genome backbone imports a prevalence-associated major antigen signature. Microb Genom. 2019;5(11):e000313. doi:10.1099/mgen.0.000313; 6. Seemann T. ABRIcate. <a href="https://github.com/tseemann/abricate">https://github.com/tseemann/abricate</a> 7 Nunes A, Nogueira PJ, Borrego MJ, Gomes JP. Adaptive evolution of the Chlamydia trachomatis dominant antigen reveals distinct evolutionary scenarios for B- and T-cell epitopes: worldwide survey. PLoS One. 2010 Oct 5;5(10):e13171. doi: 10.1371/journal.pone.0013171. PMID: 20957150; PMCID: PMC2950151. 8. Cruz H, Pinheiro M, Borges V. ReporType: a flexible bioinformatics tool for targeted loci screening and typing of infectious agents (<a href="https://github.com/insapathogenomics/ReporType">https://github.com/insapathogenomics/ReporType</a>). Int J Mol Sci. 2024;25:3172. https://doi.org/10.3390/ijms25063172

Supplementary Movie for the Paper "The 2021 and 2022 North Coast California earthquake sequences and fault complexity in the vicinity of the Mendocino Triple Junction"

Supplementary Movie for the Paper  The 2021 and 2022 North Coast California earthquake sequences and fault complexity in the vicinity of the Mendocino Triple Junction  by Margaret Hellweg, Douglas S. Dreger, Anthony Lomax, Robert C. McPherson and Lori Dengler Accepted for publication in Bulletin of the Seismological Society of America DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">TODO</a> Movie S1. Animation of the seismicity in the region of the Mendocino Triple Junction from 1982 - 2023 relocated using  NLL-SSST-coherence. See Figure 1 caption in main paper for more details.

openmit-licenseSep 2024View details →

The databases of linear-recursive and "core" sequences

These two files are linres and core databases of integer sequences related to my paper where I apply the algorithm Diofantos on them. They are also included in the related source code repository with the DOI: <div> <pre>https://doi.org/10.5281/zenodo.13692310 I created this repository separate from the program code to make it easier to find. linres : linear_database_newbl.csv core : cores_test.csv</pre> </div>

opencc-by-4.0Apr 2024View details →

Data mining antibody sequences for database searching in bottom-up proteomics

Mass spectrometry (MS)-based proteomics is a powerful method for identifying and quantifying antibodies. Among the various MS approaches, bottom-up proteomics is especially effective for analyzing thousands of antibodies in complex mixtures. In this method, proteins are enzymatically digested into smaller peptides, typically using the protease trypsin, which are then analyzed via mass spectrometry. These peptides are matched to sequences in standard databases like UniProt or NCBI-RefSeq for identification. However, a major limitation of this approach is the absence of comprehensive disease-specific antibody databases. Current databases, such as UniProt, include only a fraction of the antibody sequences present in the human body. For instance, as of January 2024, UniProt contains just 38,800 immunoglobulin sequences, far short of the billions of antibodies the human immune system can produce. As a result, relying on such limited databases can lead to under-detection of antibodies, particularly those associated with specific diseases. Expanding antibody databases with disease-specific sequences is crucial for improving the accuracy of MS-based proteomics in identifying antibodies relevant to human health. Recently, through next-generation sequencing of antibody gene repertoires, it has become possible to obtain billions of antibody sequences (in amino acid format) by annotating, translating, and numbering antibody gene sequences. These large numbers of sequences are now available in public databases such as the <a href="https://opig.stats.ox.ac.uk/webapps/oas/" rel="nofollow">Observed Antibody Space</a>. We hypothesize that using these theoretical antibody sequences as new databases for bottom-up proteomics could address the current lack of antibody coverage in standard databases. We developed a workflow to create disease-specific antibody peptide databases for bottom-up proteomics. The workflow details are available on <a href="https://github.com/trinhxt/SDU_Immunoinformatics">GitHub</a>. The database and metadata files generated by this workflow are stored in this Zenodo dataset, and they are used in DAT-DB — a web application that allows researchers to obtain FASTA files of disease-specific antibody peptides for direct use in bottom-up proteomics (see <a href="https://trinhxt.shinyapps.io/DAT-DB/">Demo version</a>). Each database file in this dataset is in .duckdb format and contains tables with 10 columns: Sequence, Filename, Patient, BSource, BType, Isotype, N_patient, N_antibody, Length_aa, and CDR3. The "Sequence" column contains tryptic peptides. "Filename" is the file where the data was collected. "Patient" refers to the patient number as listed in metadata2.csv. "BSource" refers to the B-cells' source, and "BType" refers to the type of B-cells. "Isotype" specifies the antibody isotype (IgA, IgD, IgE, IgG, IgM, or Bulk). "N_patient" indicates the number of patients having this peptide, and "N_antibody" specifies the number of antibodies containing this peptide. "Length_aa" indicates the number of amino acids in the peptide, while "CDR3" shows whether the peptide is found in the CDR3 region. The file metadata1.csv contains information about each database file, while metadata2.csv provides details about the sources of the collected antibodies.

opencc-by-4.0May 2023View details →

Data accompanying "In silico analysis of the profilaggrin sequence indicates alterations in the stability, degradation route, and intracellular protein fate in filaggrin null mutation carriers" article.

This research was supported by the National Science Centre, Poland, grant PRELUDIUM number 2021/41/N/NZ1/03473 to NS, National Science Centre, Poland, grant SONATA BIS number 2019/34/E/NZ6/00354 to DG-O, as well as POIR.04.04.00-00-21FA/16–00 grant, carried out within the First TEAM programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund (awarded to DG-O). WP was supported by the National Science Centre, Poland, grant SONATA-BIS number 2021/42/E/NZ1/00190. SB is supported by a Wellcome Trust Senior Research Fellowship (220875/Z/20/Z).

EGP Mitochondrial Genome Analysis on Human Genome Diversity Project Whole-Genome Sequencing Data

Summary: This dataset consists of running EGP version 1.3 on whole-genome sequencing data from the HGDP. The link to EGP is here https://github.com/tycheleturner/ElGenomaPequeno. Author: Tychele N. Turner, Ph.D. Short Writeup: EGP version 1.3 on Simons Genome Diversity Project: Short-read WGS CRAM files were downloaded from the EMBL-EBI Public Data Globus Endpoint from the <code>/1000g/ftp/data_collections</code> directory. Post-download, the data was run through EGP version 1.3. The results are shown below: <div> <table> <tbody> <tr> <td>Public Dataset</td> <td>EGP Result File Type</td> <td>MD5</td> </tr> <tr> <td>Human Genome Diversity Project</td> <td>Mitochondrial Genome Fasta Files for MEGA</td> <td>2b388c1fa446ecec70e33ea0471e06f8</td> </tr> <tr> <td>Human Genome Diversity Project</td> <td>Mitochondrial Genome MitoMaster Result File</td> <td>50b80ed32b1ae542c8967cc31986dd19</td> </tr> <tr> <td>Human Genome Diversity Project</td> <td>Mitochondrial Genome Variant Tables</td> <td>995f30b74c4bb094a674b1a994853246</td> </tr> <tr> <td>Human Genome Diversity Project</td> <td>Mitochondrial Genome Copy Number</td> <td>e79e61efab4c491fa2825b7d1853df58</td> </tr> </tbody> </table> </div> <div>Please note: I have found that with Zenodo you must use "Download All" for the copy number table to properly open.</div>

EGP Mitochondrial Genome Analysis on Simons Genome Diversity Project Whole-Genome Sequencing Data

Summary: This dataset consists of running EGP version 1.3 on whole-genome sequencing data from the SGDP. The link to EGP is here https://github.com/tycheleturner/ElGenomaPequeno. Author: Tychele N. Turner, Ph.D. Short Writeup: EGP version 1.3 on Simons Genome Diversity Project: Short-read WGS CRAM files were downloaded from the EMBL-EBI Public Data Globus Endpoint from the <code>/1000g/ftp/data_collections</code> directory. Post-download, the data was run through EGP version 1.3. The results are shown below: <table> <tbody> <tr> <th>Public Dataset</th> <th>EGP Result File Type</th> <th>MD5</th> </tr> </tbody> <tbody> <tr> <td>Simons Genome Diversity Project</td> <td>Mitochondrial Genome Fasta Files for MEGA</td> <td>86b09553f80926c1c29c57000ec1a88f</td> </tr> <tr> <td>Simons Genome Diversity Project</td> <td>Mitochondrial Genome MitoMaster Result File</td> <td>010026d77bee81e7b8daf5836bd12da3</td> </tr> <tr> <td>Simons Genome Diversity Project</td> <td>Mitochondrial Genome Variant Tables</td> <td>f1ea3edf4a82b42f2028467fb3544dc4</td> </tr> <tr> <td>Simons Genome Diversity Project</td> <td>Mitochondrial Genome Copy Number</td> <td>4872eeb792c214ad49662e98e4b14620</td> </tr> </tbody> </table> Please note: I have found that with Zenodo you must use "Download All" for the copy number table to properly open.

Whole-genome sequencing reveals contribution of rare and common variation to structural kidney and urinary tract malformations

Supplementary tables detailing analysis of whole-genome sequencing data from 992 patients with congenital anomalies of the kidneys and urinary tract (CAKUT).

EGP Mitochondrial Genome Analysis on 1000 Genomes Project 2504 Whole-Genome Sequencing Data

Summary: This dataset consists of running EGP version 1.3 on whole-genome sequencing data from the 1000 Genomes Project 2504 Dataset. The link to EGP is here https://github.com/tycheleturner/ElGenomaPequeno. Author: Tychele N. Turner, Ph.D. Short Writeup: EGP version 1.3 on 1000 Genomes Project 2504 Dataset: Short-read WGS CRAM files were downloaded through the paths present in this file <code>https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/1000G_2504_high_coverage.sequence.index</code>. Please note that the index files are there as well. You just have to append a <code>.crai</code>. The results are shown below: <div> <table> <tbody> <tr> <td>Public Dataset</td> <td>EGP Result File Type</td> <td>MD5</td> </tr> <tr> <td>1000 Genomes Project 2504</td> <td>Mitochondrial Genome Fasta Files for MEGA</td> <td>dbf39d6ff0e4389b900f9d985f2e6c64</td> </tr> <tr> <td>1000 Genomes Project 2504</td> <td>Mitochondrial Genome MitoMaster Result File</td> <td>4d53ef60ec16f3e4b566c45fdf0fb977</td> </tr> <tr> <td>1000 Genomes Project 2504</td> <td>Mitochondrial Genome Variant Tables</td> <td>16925b546051d37cce27df8ec57ccc5e</td> </tr> <tr> <td>1000 Genomes Project 2504</td> <td>Mitochondrial Genome Copy Number</td> <td>365c1b360ea327795d981356064658a6</td> </tr> </tbody> </table> Please note: I have found that with Zenodo you must use "Download All" for the copy number table to properly open. </div>

EGP Mitochondrial Genome Analysis on 1000 Genomes Project 698 Related Whole-Genome Sequencing Data

<div> Summary: This dataset consists of running EGP version 1.3 on whole-genome sequencing data from the 1000 Genomes Project 698 Related Dataset. The link to EGP is here https://github.com/tycheleturner/ElGenomaPequeno. Author: Tychele N. Turner, Ph.D. Short Writeup: EGP version 1.3 on 1000 Genomes Project 698 Related Dataset: Short-read WGS CRAM files were downloaded through the paths present in this file <code>https://ftp-trace.ncbi.nlm.nih.gov/1000genomes/ftp/1000G_2504_high_coverage/additional_698_related/1000G_698_related_high_coverage.sequence.index</code>. The results are shown below: <div> <table> <tbody> <tr> <td>Public Dataset</td> <td>EGP Result File Type</td> <td>MD5</td> </tr> <tr> <td>1000 Genomes Project 698 Related</td> <td>Mitochondrial Genome Fasta Files for MEGA</td> <td>322038d61b4da2e937b32410613c3532</td> </tr> <tr> <td>1000 Genomes Project 698 Related</td> <td>Mitochondrial Genome MitoMaster Result File</td> <td>36c782c12245100478903f7fa191a402</td> </tr> <tr> <td>1000 Genomes Project 698 Related</td> <td>Mitochondrial Genome Variant Tables</td> <td>68b2a51361ffae4e7ad9d420b8becd38</td> </tr> <tr> <td>1000 Genomes Project 698 Related</td> <td>Mitochondrial Genome Copy Number</td> <td>1e83c8ae132b0a7ef33b090757b29063</td> </tr> </tbody> </table> Please note: I have found that with Zenodo you must use "Download All" for the copy number table to properly open. </div>   </div> <h2> </h2>

EGP Mitochondrial Genome Analysis on Gambian Genome Variation Project Whole-Genome Sequencing Data

Summary: This dataset consists of running EGP version 1.3 on whole-genome sequencing data from the GGVP. The link to EGP is here https://github.com/tycheleturner/ElGenomaPequeno. Author: Tychele N. Turner, Ph.D. Short Writeup: EGP version 1.3 on Gambian Genome Variation Project: Short-read WGS CRAM files were downloaded from the EMBL-EBI Public Data Globus Endpoint from the <code>/1000g/ftp/data_collections</code> directory. Post-download, the data was run through EGP version 1.3. The results are shown below: <div> <table> <tbody> <tr> <td>Public Dataset</td> <td>EGP Result File Type</td> <td>MD5</td> </tr> <tr> <td>Gambian Genome Variation Project</td> <td>Mitochondrial Genome Fasta Files for MEGA</td> <td>d21e1e91e8b4c00627171fae79a1f54d</td> </tr> <tr> <td>Gambian Genome Variation Project</td> <td>Mitochondrial Genome MitoMaster Result File</td> <td>b359d1068d4f84f7746d1ebde82df29a</td> </tr> <tr> <td>Gambian Genome Variation Project</td> <td>Mitochondrial Genome Variant Tables</td> <td>ee2b93aa93d2177d92ec0f8f308b43ed</td> </tr> <tr> <td>Gambian Genome Variation Project</td> <td>Mitochondrial Genome Copy Number</td> <td>fda509ba1d2bf33fd2d6b77b92e76c03</td> </tr> </tbody> </table> Please note: I have found that with Zenodo you must use "Download All" for the copy number table to properly open. </div>

EGP Mitochondrial Genome Analysis on GIAB Whole-Genome Sequencing Data

<div> Summary: This dataset consists of running EGP version 1.3 on whole-genome sequencing data from the GIAB. The link to EGP is here https://github.com/tycheleturner/ElGenomaPequeno. Author: Tychele N. Turner, Ph.D. Short Writeup: EGP version 1.3 on GIAB: Short-read WGS CRAM files were downloaded through the paths present in this file <code>https://raw.githubusercontent.com/genome-in-a-bottle/giab_data_indexes/refs/heads/master/AshkenazimTrio/alignment.index.AJtrio_Illumina300X_wgs_novoalign_GRCh37_GRCh38_NHGRI_07282015</code> <div> <table> <tbody> <tr> <td>Public Dataset</td> <td>EGP Result File Type</td> <td>MD5</td> </tr> <tr> <td>GIAB</td> <td>Mitochondrial Genome Fasta Files for MEGA</td> <td>5eac6ec7d36307aa401fd5441b38a506</td> </tr> <tr> <td>GIAB</td> <td>Mitochondrial Genome MitoMaster Result File</td> <td>1151ae74c8e515f4f39bef816bb55d6a</td> </tr> <tr> <td>GIAB</td> <td>Mitochondrial Genome Variant Tables</td> <td>b3e342fe9827df2e399f5685f84cd4dc</td> </tr> <tr> <td>GIAB</td> <td>Mitochondrial Genome Copy Number</td> <td>3c45c19f76f71b3ccaad155565ead5e4</td> </tr> </tbody> </table> </div> <div>Please note: I have found that with Zenodo you must use "Download All" for the copy number table to properly open.</div>     </div> <h2> </h2>

opencc-by-4.0Dec 2022View details →

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

This Zenodo record contains the dataset and model weights for "Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing".   This repository contains the following files: <ul> <li> For the human dataset by Wang et al.: <ul> <li> train_val_test_split.csv containing the mapping of the correct peptide by MaxQuant to either train, validation or test set </li> <li> psms_train_val_test.csv containing the mapping of correct PSMs (scan number, raw file and correct peptide by MaxQuant) to either train, validation or test set </li> <li> updated_spectralis_test_out.csv as before containing Spectralis-EA predictions and scores on test set, as well as initial peptides and scores by Casanovo and Novor and now containing also correct peptides by MaxQuant and Spectralis-scores on the combination of Casanovo and Novor sequences (column named spectralis_score_onlyRescoring) </li> <li> spectralis_test_out_heart_analysis.csv subset of 20220822_spectralis_test_out.csv containing only PSMs for the tissue heart with the computation of precision and recall values </li> <li> spectralis_test_out_pointnovo_deepnovo.csv containing predictions by DeepNovo and PointNovo with original scores and Spectralis-score, as well as correct peptides by MaxQuant </li> </ul> </li> </ul>   <ul> <li> For the nine-species dataset by Tran et al.: <ul> <li> spectralis_ninespecies_out.csv containing spectrum identifiers, correct peptides by PEAKSDB, predicted peptides by the different de novo sequencing tools as well as original scores and Spectralis-scores for the different PSMs. </li> </ul> </li> </ul>

DAS Data for the figure in the paper entitled "A Hybrid Earthquake Detection Method for Distributed Acoustic Sensing Array Data and Its Application to the 2022 Menyuan Earthquake Sequence"

The DAS data can be loaded using numpy. The sampling rate is 100 Hz, and each row is a time series for that channel.

Feces - deep sequencing data

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Feces - shallow sequencing data

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Sewage - shallow sequencing data

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